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Record W2769142255 · doi:10.1002/9783527651733.ch19

Metal‐Catalyzed Multicomponent Reactions

2017· other· en· W2769142255 on OpenAlexaff
Jeffrey S. Quesnel, Bruce A. Arndtsen

Bibliographic record

Venuenot available
Typeother
Languageen
FieldChemistry
TopicMulticomponent Synthesis of Heterocycles
Canadian institutionsMcGill University
Fundersnot available
KeywordsCycloadditionCatalysisCarbonylationChemistryTransition metalCarbon monoxideCoupling reactionMetalComputational chemistryMetal carbonylCombinatorial chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Multicomponent coupling reactions have become of growing importance in the design of efficient synthetic methods. This chapter highlights some of the important general classes of the metal-catalyzed multicomponent reactions, with a focus upon those that use building blocks that are all, or nearly all, readily available, and therefore of potential industrial relevance. A common approach to metal-catalyzed multicomponent reactions involves exploiting carbonylation chemistry. The incorporation of carbon monoxide into products often involves a multicomponent reaction, where the carbonyl unit in the product is linked to two separate fragments. Transition metal-catalyzed multicomponent reactions have been employed to build up products that are themselves intermediates in subsequent, non-metal based reactions. While examples of these reactions are described above, one general manifold involves generating substrates for cycloaddition reactions, such as 1,3-dipoles for use in dipolar cycloaddition reactions. The transition metal catalyzed formation of 1,3-dipoles has been applied to multicomponent syntheses in a number of directions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.005

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.281
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

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